BARCHAN: Blob Alignment for Robust CHromatographic ANalysis

Camille Couprie 1, 2 Laurent Duval 3, 2 Maxime Moreaud 2 Sophie Hénon 2 Mélinda Tebib 2 Vincent Souchon 2
1 FAIR - Facebook AI Research [Paris]
2 IFPEN - IFP Energies nouvelles
3 UPE - Université Paris-Est
Abstract : (Comprehensive) Two dimensional gas chromatography (GCxGC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templates of blobs are defined and superimposed on a reference chromatogram. The templates then need to be modified when different chromatograms are recorded. In this study, we present a chromatogram and template alignment method based on peak registration called BARCHAN. Peaks are identified using a robust mathematical morphology tool. The alignment is performed by a probabilistic estimation of a rigid transformation along the first dimension, and a non-rigid transformation in the second dimension, taking into account noise, outliers and missing peaks in a fully automated way. Resulting aligned chromatograms and masks are presented on two datasets. The proposed algorithm proves to be fast and reliable. It significantly reduces the time to results for GCxGC analysis.
Keywords : Peak registration Retention time alignment GCGC Two dimensional Gas Chromatography Chemometrics Quanti cation
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Article dans une revue
Journal of Chromatography A, Elsevier, 2017, 1484 (10), pp.65-72. 〈10.1016/j.chroma.2017.01.003〉
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Soumis le : samedi 25 février 2017 - 22:11:11
Dernière modification le : mardi 15 mai 2018 - 14:50:03
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Camille Couprie, Laurent Duval, Maxime Moreaud, Sophie Hénon, Mélinda Tebib, et al.. BARCHAN: Blob Alignment for Robust CHromatographic ANalysis. Journal of Chromatography A, Elsevier, 2017, 1484 (10), pp.65-72. 〈10.1016/j.chroma.2017.01.003〉. 〈hal-01392910〉

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